[A heart sound classification method based on complete ensemble empirical modal decomposition with adaptive noise permutation entropy and support vector machine].

Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%-24%, which demonstrates the efficiency of the proposed method.针对心音信号非平稳性、非线性的特征，为了更直观地把心音信号的特征显示出来，提高分类识别的高效性，提出了一种自适应噪声完备经验模态分解（CEEMDAN）排列熵作为心音信号的特征向量，通过支持向量机（SVM）进行心音分类识别的方法。首先，将原始心音信号进行CEEMDAN，得到若干从高频到低频的模态分量（IMF）。其次，利用IMF分量与原始信号的相关系数、能量因子和信噪比来优选IMF做Hilbert变换，得到分量信号的瞬时频率，再计算各IMF排列熵值组成特征向量。最后，将特征向量输入SVM二分类器进行正常与异常心音信号的分类识别。对源自2016年PhysioNet/CinC挑战赛的100例心音样本进行正常与异常的分类，准确度达到87%。研究表明本文方法相比于常用的EMD和EEMD排列熵的方法准确度提高了18%～24%，可见，CEEMDAN排列熵结合SVM的方法能够有效识别正常和异常心音。.